Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering problems. In these problems, the “training” data often consists simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation, or whether or not a user has bookmarked a page in the bookmarking scenario. At the scale of the web, this type of data is extremely sparse.
Because of this sparsity, there is an ambiguity in the interpretation of the so-called negative examples. For example, one cannot really attribute a user's inaction of not bookmarking a webpage to a lack of interest in the page as opposed to a simple lack of awareness of the page. In other words, negative examples and unlabeled positive examples are combined together, and it is typically not possible to distinguish between them.
Labeling negative examples to convert them into a classical CF problem is very expensive, or even intractable, because users simply do not wish to bear the burden of doing so. Previous research addressing this one-class problem only considered it as a classification task. However, little work has been done on this problem in the CF setting.